8,899 research outputs found
Self-supervised learning: When is fusion of the primary and secondary sensor cue useful?
Self-supervised learning (SSL) is a reliable learning mechanism in which a
robot enhances its perceptual capabilities. Typically, in SSL a trusted,
primary sensor cue provides supervised training data to a secondary sensor cue.
In this article, a theoretical analysis is performed on the fusion of the
primary and secondary cue in a minimal model of SSL. A proof is provided that
determines the specific conditions under which it is favorable to perform
fusion. In short, it is favorable when (i) the prior on the target value is
strong or (ii) the secondary cue is sufficiently accurate. The theoretical
findings are validated with computational experiments. Subsequently, a
real-world case study is performed to investigate if fusion in SSL is also
beneficial when assumptions of the minimal model are not met. In particular, a
flying robot learns to map pressure measurements to sonar height measurements
and then fuses the two, resulting in better height estimation. Fusion is also
beneficial in the opposite case, when pressure is the primary cue. The analysis
and results are encouraging to study SSL fusion also for other robots and
sensors
Regulation Theory
This paper reviews the design of regulation loops for power converters. Power
converter control being a vast domain, it does not aim to be exhaustive. The
objective is to give a rapid overview of the main synthesis methods in both
continuous- and discrete-time domains.Comment: 23 pages, contribution to the 2014 CAS - CERN Accelerator School:
Power Converters, Baden, Switzerland, 7-14 May 201
Recommended from our members
A Tablet-Based Assessment of Rhythmic Ability.
The exponential rise in use of mobile consumer electronics has presented a great potential for research to be conducted remotely, with participants numbering several orders of magnitude greater than a typical research paradigm. Here, we attempt to demonstrate the validity and reliability of using a consumer game-engine to create software presented on a mobile tablet to assess sensorimotor synchronization, a proxy of rhythmic ability. Our goal was to ascertain whether previously observed research results can be replicated, rather than assess whether a mobile tablet achieves comparable performance to a desktop computer. To achieve this, younger (aged 18-35 years) and older (aged 60-80 years) adult musicians and non-musicians were recruited to play a custom-designed sensorimotor synchronization assessment on a mobile tablet in a controlled laboratory environment. To assess reliability, participants performed the assessment twice, separated by a week, and an intra-class correlation coefficient (ICC) was calculated. Results supported the validity of this approach to assessing rhythmic abilities by replicating previously observed results. Specifically, musicians performed better than non-musicians, and younger adults performed better than older adults. Participants also performed best when the tempo was in the range of previously-identified preferred tempos, when the stimuli included both audio and visual information, and when synchronizing on-beat compared to off-beat or continuation (self-paced) synchronization. Additionally, high ICC values (>0.75) suggested excellent test-retest reliability. Together, these results support the notion that consumer electronics running software built with a game engine may serve as a valuable resource for remote, mobile-based data collection of rhythmic abilities
- …